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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The field of [NILM](http://en.wikipedia.org/wiki/Nonintrusive_load_monitoring) has seen some exciting developments in the recent past. However, with the release of [nilmtk](http://nilmtk.github.io/), we are aiming to see how well different approaches line up. So, we were interested in bringing one of the [seminal work](http://georgehart.com/research/Hart1985.pdf) in NILM by [George Hart](http://georgehart.com/) to nilmtk and give it a spin. In this notebook, I'll present a very quick overview of the algorithm and test it out on a home from the [WikiEnergy](https://dataport.pecanstreet.org/) data set."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Customary imports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "from os.path import join\n",
      "\n",
      "from pylab import rcParams\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "rcParams['figure.figsize'] = (12, 6)\n",
      "\n",
      "import nilmtk\n",
      "from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore\n",
      "from nilmtk.disaggregate import CombinatorialOptimisation\n",
      "from nilmtk.utils import print_dict\n",
      "from nilmtk.metrics import f1_score\n",
      "import seaborn as sns\n",
      "sns.set_palette(\"Set3\", n_colors=12)\n",
      "\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings(\"ignore\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/nipun/git/nilmtk/nilmtk/elecmeter.py:4: DeprecationWarning: The compiler package is deprecated and removed in Python 3.x.\n",
        "  from compiler.ast import flatten\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Version information"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Loading data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_dir = '/home/nipun/Copy/Dataset/'\n",
      "we = DataSet(join(data_dir, 'iawe.h5'))\n",
      "print('loaded ' + str(len(we.buildings)) + ' buildings')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "loaded 1 buildings\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Examine metadata for a single house"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "building_number = 1\n",
      "print_dict(we.buildings[building_number].metadata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<ul><li><strong>instance</strong>: 1</li><li><strong>dataset</strong>: iAWE</li><li><strong>original_name</strong>: house_1</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f6ce56230d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Let us perform our analysis on the first 2 days"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "we.store.window = TimeFrame(start='2013-05-24 00:00:00+05:30', end='2013-05-26 00:00:00+05:30')\n",
      "elec = we.buildings[building_number].elec\n",
      "elec.appliances"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[Appliance(type='fridge', instance=1),\n",
        " Appliance(type='unknown', instance=1),\n",
        " Appliance(type='washing machine', instance=1),\n",
        " Appliance(type='clothes iron', instance=1),\n",
        " Appliance(type='television', instance=1),\n",
        " Appliance(type='wet appliance', instance=1),\n",
        " Appliance(type='air conditioner', instance=1),\n",
        " Appliance(type='air conditioner', instance=2),\n",
        " Appliance(type='motor', instance=1),\n",
        " Appliance(type='computer', instance=1)]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mains = elec.mains()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let us quickly have a look at what contribution different appliances have to the mains."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mains.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f6caf88c810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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NmRwsb1u8llnlzNgkedtirZBjIhipeI9t21jYmDg1KptxNLfOd+b3YANZuwA4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\nsUVC5sbuwIiqvl5Vzwe+DLwKWAt8BThHRNambS8HvIgsWZi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DMAzDMAzDMAzDMAzDMAzD\nMAzDMAzDMAzDMAyjF/l/zPE09oJF/hcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6ce5608f90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.plot()\n",
      "plt.tight_layout()\n",
      "plt.ylabel(\"Power (W)\")\n",
      "plt.xlabel(\"Time\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OqbOYDCd82hZtjGRwhBCiR1RmhQZqbv7bosmqnx9aOlQ/XkhH9fZDdDAZgRiI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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6ce5623190>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From the plot above, a human could easily see the signature of 3 appliances: `Fridge`, `Air conditioner` and `Electric furnace`\n",
      "    \n",
      "We'll hope that our unsupervised implementation would be able to figure out these 3 appliances.    "
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Another way of understanding contribution of different appliances is via the following pie chart"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fraction = elec.submeters().fraction_per_meter().dropna()\n",
      "labels = elec.get_appliance_labels(fraction.index)\n",
      "plt.figure(figsize=(8,8))\n",
      "fraction.plot(kind='pie', labels=labels);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1/10 ElecMeter(instance=3, building=1, dataset='iAWE', appliances=[Appliance(type='fridge', instance=1)])"
       ]
      },
      {
       "ename": "ValueError",
       "evalue": "cannot use label indexing with a null key",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-13-683776b415f6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfraction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0melec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubmeters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfraction_per_meter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0melec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_appliance_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfraction\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mfraction\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'pie'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/metergroup.pyc\u001b[0m in \u001b[0;36mfraction_per_meter\u001b[1;34m(self, **load_kwargs)\u001b[0m\n\u001b[0;32m   1003\u001b[0m         \u001b[0mEach\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0ma\u001b[0m \u001b[0mfloat\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrange\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1004\u001b[0m         \"\"\"\n\u001b[1;32m-> 1005\u001b[1;33m         \u001b[0menergy_per_meter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menergy_per_meter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mload_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1006\u001b[0m         \u001b[0mtotal_energy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menergy_per_meter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1007\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0menergy_per_meter\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mtotal_energy\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/metergroup.pyc\u001b[0m in \u001b[0;36menergy_per_meter\u001b[1;34m(self, **load_kwargs)\u001b[0m\n\u001b[0;32m    993\u001b[0m             \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\r{:d}/{:d} {}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_meters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    994\u001b[0m             \u001b[0mstdout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 995\u001b[1;33m             \u001b[0mmeter_energy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtotal_energy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mload_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    996\u001b[0m             \u001b[0menergy_per_meter\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmeter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midentifier\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeter_energy\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    997\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0menergy_per_meter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'all'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/elecmeter.pyc\u001b[0m in \u001b[0;36mtotal_energy\u001b[1;34m(self, **loader_kwargs)\u001b[0m\n\u001b[0;32m    439\u001b[0m         \u001b[0mnodes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mClip\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTotalEnergy\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    440\u001b[0m         return self._get_stat_from_cache_or_compute(\n\u001b[1;32m--> 441\u001b[1;33m             nodes, TotalEnergy.results_class(), loader_kwargs)\n\u001b[0m\u001b[0;32m    442\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    443\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdropout_rate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mloader_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/elecmeter.pyc\u001b[0m in \u001b[0;36m_get_stat_from_cache_or_compute\u001b[1;34m(self, nodes, results_obj, loader_kwargs)\u001b[0m\n\u001b[0;32m    519\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mloader_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'preprocessing'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    520\u001b[0m             \u001b[0mcached_stat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_cached_stat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey_for_cached_stat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 521\u001b[1;33m             \u001b[0mresults_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimport_from_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcached_stat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msections\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    522\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    523\u001b[0m             \u001b[1;31m# Get sections_to_compute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/results.pyc\u001b[0m in \u001b[0;36mimport_from_cache\u001b[1;34m(self, cached_stat, sections)\u001b[0m\n\u001b[0;32m    151\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0msection\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msections\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 153\u001b[1;33m                 \u001b[0mrows_matching_start\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcached_stat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    154\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1192\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1193\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1194\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1195\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1196\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1335\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1336\u001b[0m         \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1337\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1338\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1339\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_has_valid_type\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1297\u001b[0m                 \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1299\u001b[1;33m                 \u001b[0merror\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1301\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36merror\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1282\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1283\u001b[0m                     raise ValueError(\n\u001b[1;32m-> 1284\u001b[1;33m                         \"cannot use label indexing with a null key\")\n\u001b[0m\u001b[0;32m   1285\u001b[0m                 raise KeyError(\"the label [%s] is not in the [%s]\" %\n\u001b[0;32m   1286\u001b[0m                                (key, self.obj._get_axis_name(axis)))\n",
        "\u001b[1;31mValueError\u001b[0m: cannot use label indexing with a null key"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The air conditioner, fridge and electric furnace together consume around 80% of total energy."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Training"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll now do the training from the aggregate data. The algorithm segments the time series data into steady and transient states. Thus, we'll first figure out the transient and the steady states. Next, we'll try and pair the on and the off transitions based on their proximity in time and value. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from nilmtk.disaggregate.hart_85 import Hart85\n",
      "h = Hart85()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h.train(mains)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "load() got an unexpected keyword argument 'load_all_power_columns'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-15-75ddc06f7ce8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmains\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/disaggregate/hart_85.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, metergroup, cluster_features, bsize, minTol)\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m         \"\"\"\n\u001b[1;32m--> 226\u001b[1;33m         \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msteady_states\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransients\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfind_steady_states_transients\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmetergroup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    228\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpair_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpair\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mminTol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/feature_detectors/steady_states.py\u001b[0m in \u001b[0;36mfind_steady_states_transients\u001b[1;34m(metergroup)\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[0mtransients_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mpower_df\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmetergroup\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpower_series\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_all_power_columns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpower_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m             \u001b[1;31m# Use whatever is available\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/electric.pyc\u001b[0m in \u001b[0;36mpower_series\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    491\u001b[0m         \u001b[1;31m# Pull data through preprocessing pipeline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    492\u001b[0m         \u001b[0mgenerator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 493\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    494\u001b[0m             \u001b[0mchunk_to_yield\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mchunk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0micol\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m             \u001b[0mchunk_to_yield\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'timeframe'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/metergroup.pyc\u001b[0m in \u001b[0;36mload\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    565\u001b[0m         \"\"\"\n\u001b[0;32m    566\u001b[0m         \u001b[1;31m# Get a list of generators\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 567\u001b[1;33m         \u001b[0mgenerators\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmeter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mmeter\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeters\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    568\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    569\u001b[0m         \u001b[1;31m# Load each generator and yield the sum\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/elecmeter.pyc\u001b[0m in \u001b[0;36mload\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    400\u001b[0m         \u001b[1;31m# Get source node\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[0mpreprocessing\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'preprocessing'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m         \u001b[0mlast_node\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_source_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    403\u001b[0m         \u001b[0mgenerator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlast_node\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    404\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/elecmeter.pyc\u001b[0m in \u001b[0;36mget_source_node\u001b[1;34m(self, **loader_kwargs)\u001b[0m\n\u001b[0;32m    421\u001b[0m             raise RuntimeError(\n\u001b[0;32m    422\u001b[0m                 \"Cannot get source node if meter.store is None!\")\n\u001b[1;32m--> 423\u001b[1;33m         \u001b[0mgenerator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mloader_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    424\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'device'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    425\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mNode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/docinherit.pyc\u001b[0m in \u001b[0;36mf\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     44\u001b[0m         \u001b[1;33m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmthd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0massigned\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'__name__'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'__module__'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmthd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_parent_doc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moverridden\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mTypeError\u001b[0m: load() got an unexpected keyword argument 'load_all_power_columns'"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We obtain the set of steady states as follows (only top 10 entries shown for ease of view)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h.steady_states.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>active average</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:00:00-05:00</th>\n",
        "      <td> 755.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:06:00-05:00</th>\n",
        "      <td> 607.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:09:00-05:00</th>\n",
        "      <td> 505.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:25:00-05:00</th>\n",
        "      <td> 358.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:31:00-05:00</th>\n",
        "      <td> 502.090909</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "                           active average\n",
        "2014-04-01 00:00:00-05:00      755.500000\n",
        "2014-04-01 00:06:00-05:00      607.500000\n",
        "2014-04-01 00:09:00-05:00      505.250000\n",
        "2014-04-01 00:25:00-05:00      358.666667\n",
        "2014-04-01 00:31:00-05:00      502.090909"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The table above shows the steady power states observed in the mains data. Another way of understanding the same is by looking at the start of steady states overlayed on top of mains power consumption, shown as follows."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.set_palette(\"Set1\", n_colors=12)\n",
      "ax = mains.plot()\n",
      "h.steady_states['active average'].plot(style='o', ax = ax);\n",
      "plt.ylabel(\"Power (W)\")\n",
      "plt.xlabel(\"Time\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qlEVNba9y5/9zvj62BXj2WWBkBBgfbzqgN1myaBDr/uI0FF/6+7Be9gpn9sLe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\ngiAIgiAIgiAIgiAIgiAIgiAIgtC3+P8TgVzaCHTXtgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fafaefb1c50>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The difference b/w successive steady states gives us the transitions b/w them. The following table shows the obtained transitions (again only top 10 entries shown)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h.transients.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>active transition</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:00:00-05:00</th>\n",
        "      <td> 755.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:06:00-05:00</th>\n",
        "      <td>-148.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:09:00-05:00</th>\n",
        "      <td>-102.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:25:00-05:00</th>\n",
        "      <td>-146.583333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:31:00-05:00</th>\n",
        "      <td> 143.424242</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "                           active transition\n",
        "2014-04-01 00:00:00-05:00         755.500000\n",
        "2014-04-01 00:06:00-05:00        -148.000000\n",
        "2014-04-01 00:09:00-05:00        -102.250000\n",
        "2014-04-01 00:25:00-05:00        -146.583333\n",
        "2014-04-01 00:31:00-05:00         143.424242"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These transitions when seen on the time series would appear as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h.transients['active transition'].plot(style='o')\n",
      "plt.ylabel(\"Power (W)\")\n",
      "plt.xlabel(\"Time\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xd55p15QHM/OrAJl5A3BA1RVrUhUzMM211+gHgxKvKpZPNsPeZNt3s++ZfA46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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fafaef40050>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, we pair the on (positive) and the off (negative) transitions as shown in the table below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h.pair_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>T1 Time</th>\n",
        "      <th>T1 Active</th>\n",
        "      <th>T2 Time</th>\n",
        "      <th>T2 Active</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2014-04-01 05:31:00</td>\n",
        "      <td> 143.424242</td>\n",
        "      <td>2014-04-01 05:43:00</td>\n",
        "      <td>-145.690909</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2014-04-01 05:49:00</td>\n",
        "      <td> 144.145455</td>\n",
        "      <td>2014-04-01 06:01:00</td>\n",
        "      <td>-138.212121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2014-04-01 06:08:00</td>\n",
        "      <td> 141.484848</td>\n",
        "      <td>2014-04-01 06:19:00</td>\n",
        "      <td>-145.984848</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2014-04-01 06:26:00</td>\n",
        "      <td> 144.766667</td>\n",
        "      <td>2014-04-01 06:37:00</td>\n",
        "      <td>-145.600000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2014-04-01 06:44:00</td>\n",
        "      <td> 145.600000</td>\n",
        "      <td>2014-04-01 06:55:00</td>\n",
        "      <td>-143.100000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "              T1 Time   T1 Active             T2 Time   T2 Active\n",
        "0 2014-04-01 05:31:00  143.424242 2014-04-01 05:43:00 -145.690909\n",
        "1 2014-04-01 05:49:00  144.145455 2014-04-01 06:01:00 -138.212121\n",
        "2 2014-04-01 06:08:00  141.484848 2014-04-01 06:19:00 -145.984848\n",
        "3 2014-04-01 06:26:00  144.766667 2014-04-01 06:37:00 -145.600000\n",
        "4 2014-04-01 06:44:00  145.600000 2014-04-01 06:55:00 -143.100000"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following image borrowed from Hart's paper summarises the above discussion."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://i.imgur.com/vU2ZgD4.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once, we obtain these pairings, we consider a particular appliance usage as the average of the magnitude of the on and the off transition. Then, we use a non-parametric clustering method to obtain the number of appliances and the corresponding power draws. This step in our implementation is different from what was originally proposed by George Hart. Once done, we obtain the learnt appliances as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h.centroids"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>active</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  146.458708</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 3532.218934</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  911.500000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "        active\n",
        "0   146.458708\n",
        "1  3532.218934\n",
        "2   911.500000"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So, we obtain 3 appliances as per our unsupervised learning algorithm. Based on the power draw, I think I see air conditioner and refrigerator there. What is the third one? Not sure."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Disaggregation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Disaggregation follows much of the same method as training. During disaggregation, we also assign transients from the learnt model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "disag_filename = join(data_dir, 'wikienergy-disag.h5')\n",
      "output = HDFDataStore(disag_filename, 'w')\n",
      "h.disaggregate(mains, output)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Finding Edges, please wait ...\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Edge detection complete.\n",
        "Creating transition frame ...\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Transition frame created.\n",
        "Creating states frame ...\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "States frame created.\n",
        "Finished.\n",
        "States done"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0\n",
        "1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "2\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "output.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Comparison with ground truth"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since we used an unsupervised approach, we don't have labels (as of yet) for the learnt appliances. Thus, we can't directly use accuracy metrics directly (as of yet, maybe in a newer version we can). I'll compare the power draw using our disaggregation with ground truth manually."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "disag = DataSet(disag_filename)\n",
      "disag_elec = disag.buildings[building_number].elec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Comparing for Fridge"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax1 = disag_elec['unknown', 0].plot()\n",
      "ax2 = elec['fridge', 1].plot()\n",
      "ax1.legend([\"Predicted\", \"Ground truth\"]);\n",
      "plt.ylabel(\"Power (W)\")\n",
      "plt.xlabel(\"Time\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gU7VDZZEI8DQz8llX7SIiEo3qxhiF2hnJkkg0a21gvd8wjCz/A8MwzgT2OVoi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nQ40JCxvae+DS1a7bcU3mFgIFmTj8jAbbTFP7xAhtqngFJCna3k8CK0DMC61125XOAPHwlzEctN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\nOjr2i/8foMrzwQ+EO0EAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fafaf06c110>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Comparing for air conditioner"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax1 = disag_elec['unknown', 1].plot()\n",
      "ax2 = elec['air conditioner', 1].plot()\n",
      "ax1.legend([\"Predicted\", \"Ground truth\"]);\n",
      "plt.ylabel(\"Power (W)\")\n",
      "plt.xlabel(\"Time\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7A/CDdZ08fNRbAdgyNsUVdzzTNkU7yyP5Vfsv4th9BrjzyU3c/sTGTDIrtSrH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\nrvvQNZFXYfbXPl6wJpvCHrhoJwyc6fym/TcjYQb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ev7pc1PosGppYiCK026yxGZqXhHZ9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eszxr0a2vbFyfOlXjrWdV8/WbQvxV13nUFyYN5Kk0U6SaLVuVPSkWsAvF0p9T4A/wjgz/NwfT8L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\nhBBCCCGEEEIIIYSQvcr/D8d08XJaqoc7AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fafaef089d0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "While the timing looks impeccable, there seems to be some ambiguity in the power measurement :("
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Comparing for the `unknown` appliance"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax1 = disag_elec['unknown', 2].plot()\n",
      "ax2 = elec['electric furnace', 1].plot()\n",
      "ax1.legend([\"Predicted\", \"Ground truth\"]);\n",
      "plt.ylabel(\"Power (W)\")\n",
      "plt.xlabel(\"Time\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bh/G9jx2JPee1vhyM6X+bebaTfd8/Vin1cMutIIQQMqF4qVlLD7y0Hg+8tB5/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\nZ0uOViR7EFQuoiiKogyNd+c34hxIX92oHNcVz9paucCA9XPqDgen7xy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Fwg5ipt17+1AK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\nURRFUZSVyP8HfhY2DFG4i98AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fafaec9b950>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So, this doesn't look to be a good fit. Maybe, the dish washer?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax1 = disag_elec['unknown', 2].plot()\n",
      "ax2 = elec['dish washer', 1].plot()\n",
      "ax1.legend([\"Predicted\", \"Ground truth\"]);\n",
      "plt.ylabel(\"Power (W)\")\n",
      "plt.xlabel(\"Time\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2K1+JiIiIVCllsif3NERaV7tPdjOFHLUmo8Gj8evCS7JRNmU3yM7n8ykiIiJS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\nI8zsxMj6p4CjzOyW6NdLYVbxIGA3M7uo5gPkTGQ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       "text": [
        "<matplotlib.figure.Figure at 0x7fafae422450>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Probably, a decent fit. Not really sure though!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Broadly speaking, this 30 year algorithm seems to be doing a fairly decent job. The fact that this is unsupervised makes it all the more useful. There is some more work to be done on this algorithm. So, watch out for more posts and updates. Looking forward to your comments and feedback."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}